Kai Gets Internet Access: Turning Context Into Intelligence for Product Security Teams

For years, product security teams have lived with a gap. Tools surfaced findings — CVEs, outdated packages, risky dependencies — but rarely the context to make sense of them. Engineers still had to open a browser, type a CVE into Google, skim through NVD, vendor advisories, GitHub issues, and random blogs to answer basic questions: Is this actually exploitable in our environment? Is there a safe upgrade path? Has anyone seen this exploited in the wild?

This release closes that gap.

Kai now has secure, controlled access to the internet — to trusted, curated sources like NVD, CISA KEV, Maven Central, PyPI, and high-fidelity research blogs. It’s the first time Kai steps beyond the Kodem dataset and starts reasoning with real-world intelligence. For security engineers, it’s not a chatbot upgrade. It’s a shift from platform awareness to situational awareness.

Why It Matters for Product Security

Moon Active’s AppSec team told us their engineers spend “hours Googling for context” on vulnerabilities. MinuteMedia’s security leads said they needed a faster way to “vet new open-source libraries before adoption.”

These aren’t edge cases. They’re everyday pain points for security engineers: long triage cycles, inconsistent risk assessments, and uncertainty around which findings deserve attention now.

By letting Kai reason over live, external data, teams can now get immediate, LLM-optimized answers that bridge that context gap.

From Findings to Full Context

Ask Kai about a vulnerability, and it doesn’t stop at what’s in your SBOM. It fetches CVE data, checks exploit status, references multiple vendor advisories, and notes any discrepancies. Ask it for the “safest upgrade” for a dependency, and it not only identifies a patched version but shows its ecosystem adoption rate — a real proxy for stability.

This makes conversations like these possible inside Kodem:

• “We’re on requests 2.25.0 — what’s the most secure version we can upgrade to without breaking compatibility?”
• “Is spring-boot 2.7.5 vulnerable to any CVEs we should care about?”
• “Who’s exploiting CVE-2023-38545, and how?”

Each answer includes transparent attribution and a visible chain of thought — so engineers can see where the data came from and how Kai reached its conclusion.

Faster, Smarter Decisions

When Kai enriches every finding with real-world context, Mean Time to Triage drops sharply. Security engineers spend less time searching and more time deciding. AppSec leads gain confidence that risk assessments reflect current threat reality, not static CVSS scores.

Kai also enables proactive security: engineers can now ask, before merging code, “Is this library healthy?” and get an answer grounded in version activity, maintenance signals, and deprecation data. It’s a simple question, but one that prevents a long tail of technical debt later.

Built for Engineers, Not Marketing

Every new capability follows strict boundaries: Kai only queries allow-listed sources, never leaks proprietary data, and always exposes citations. When explaining software licenses, it automatically includes legal disclaimers. When reasoning over public exploits, it makes the provenance explicit. Transparency isn’t optional — it’s the point.

The value here isn’t novelty. It’s trust. Product security engineers can now use LLM answers without sacrificing verifiability.

What We’re Seeing So Far

• Teams using Kai for vulnerability research have reported reductions of up to 40% in triage time compared to manual web searches.‍
• Dependency vetting that previously took hours — checking repo activity, licenses, and CVEs — now takes seconds.
• Findings are enriched automatically with exploit-in-the-wild signals, leading to more accurate prioritization and fewer false-urgent tickets.
• Kai’s context transparency — showing how it reached each conclusion — has become a training and onboarding accelerator for new AppSec engineers.

These are early results, but the pattern is clear: when the research friction disappears, engineering speed goes up — without sacrificing accuracy.

The Bottom Line

Kai’s internet access turns vulnerability data into intelligence: verified, attributed, and explainable.
It integrates external context directly into your workflow, letting teams reason about real-world risk instead of static metadata.

Product security is moving toward continuous, contextualized intelligence. With this release, Kai makes that possible today.

See it in action — book a demo and experience how context-aware intelligence changes product security.